JP7111424B2 - Mobile object, position estimation device, and computer program - Google Patents

Description

The present disclosure relates to mobile objects, position estimation devices, and computer programs.

Autonomous mobile bodies such as automated guided vehicles (automated guided vehicles) and mobile robots are being developed.

Japanese Patent Application Laid-Open No. 2008-250906 discloses a mobile robot that estimates its own position by matching a local map obtained from a laser range finder with a map prepared in advance.

JP 2008-250906 A

When performing matching, the mobile robot disclosed in Japanese Patent Application Laid-Open No. 2008-250906 uses the output of an optical fiber gyro and the output of a rotary encoder (hereinafter simply referred to as "encoder") attached to a motor or drive wheel. is used to estimate the self-position.

Embodiments of the present disclosure provide a mobile object capable of self-position estimation without using the output of an encoder.

In a non-limiting exemplary embodiment, the mobile body of the present disclosure comprises: an external sensor that scans the environment and periodically outputs scan data; a storage device that stores an environment map; a self-position estimation device that performs matching with the environment map read out from the device and estimates the position and orientation of the moving object. The self-position estimation device determines a predicted value of the current position and estimation of the moving object based on the history of the estimated position and the estimated attitude of the moving object, and performs the matching using the predicted value.

A position estimation device of the present disclosure, in a non-limiting exemplary embodiment, is a mobile device connected to an external sensor that scans the environment and outputs scan data periodically, and a storage device that stores a map of the environment. A body position estimation device comprising a processor and a memory storing a computer program for operating the processor. The processor, according to instructions of the computer program, determines a predicted value of the current position and the estimated attitude of the moving object based on a history of the estimated position and the estimated attitude of the moving object; Using this, matching is performed between the sensor data and the environment map read out from the storage device, and the position and orientation of the moving object are estimated.

A computer program of the present disclosure, in a non-limiting exemplary embodiment, is a computer program for use in the position estimation device described above.

According to the embodiment of the mobile object of the present disclosure, self-position estimation can be performed without using the output of the encoder.

FIG. 1 is a diagram showing the configuration of an embodiment of a mobile object according to the present disclosure. FIG. 2 is a plan layout diagram schematically showing an example of an environment in which a moving object moves. 3 is an environmental map of the environment shown in FIG. 2; FIG. FIG. 4 is a diagram schematically showing an example of scan data SD(t) acquired by the external sensor at time t. FIG. 5A is a diagram schematically showing a state when matching of scan data SD(t) with an environment map is started. FIG. 5B is a diagram schematically showing a state in which matching of the scan data SD(t) with respect to the environment map is completed. FIG. 6A is a diagram schematically showing how a point group that constitutes scan data rotates and translates from its initial position and approaches the point group of the environment map. FIG. 6B is a diagram showing the position and orientation of scan data after rigid transformation. FIG. 7 is a diagram schematically showing an example of scan data SD(t+Δt) acquired by the external sensor at time t+Δt. FIG. 8A is a diagram schematically showing a state when matching of scan data SD(t+Δt) with an environment map is started. FIG. 8B schematically shows a state in which predicted values calculated based on the history of the position and orientation are used as the initial values of the position and orientation of the moving object when matching of the scan data SD(t+Δt) with the environment map is started. is a diagram shown in FIG. FIG. 9 is a diagram schematically showing the history of the position and orientation of the moving object obtained in the past by the position estimation device and the predicted values of the current position and orientation. FIG. 10 is a flow chart showing part of the operation of the position estimation device in the embodiment of the present disclosure. FIG. 11 is a flow chart showing part of the operation of the position estimation device according to the embodiment of the present disclosure. FIG. 12 is a diagram showing another configuration example of the mobile object of the present disclosure. FIG. 13 is a schematic diagram of a control system that controls travel of each AGV according to the present disclosure. FIG. 14 is a perspective view showing an example of an environment in which an AGV exists. FIG. 15 is a perspective view showing the AGV and towing truck before being connected. FIG. 16 is a perspective view showing the connected AGV and tow truck. FIG. 17 is an external view of an exemplary AGV according to this embodiment. FIG. 18A is a diagram showing a first hardware configuration example of the AGV. FIG. 18B is a diagram showing a second hardware configuration example of the AGV. FIG. 19 is a diagram showing a hardware configuration example of an operation management device.

<Term>
"Automated Guided Vehicle" (AGV) means a trackless vehicle that loads cargo manually or automatically into its main body, travels automatically to a designated location, and unloads manually or automatically. "Automated guided vehicle" includes automated towing vehicles and automated forklifts.

The term "unmanned" means that no person is required to steer the vehicle, and does not exclude that the AGV carries "persons (e.g., those loading and unloading goods)".

An “unmanned tractor” is a trackless vehicle that automatically travels to a designated location by towing a trolley that loads and unloads cargo manually or automatically.

An “unmanned forklift” is a trackless vehicle equipped with a mast that raises and lowers a fork for loading and unloading cargo.

A "trackless vehicle" is a vehicle that has wheels and an electric motor or engine that rotates the wheels.

A “moving body” is a device that moves with a person or load on it, and includes driving devices such as wheels, a bipedal or multipedal walking device, and a propeller that generate a driving force (traction) for movement. The term “mobile” in the present disclosure includes mobile robots, service robots, and drones as well as automated guided vehicles in the narrow sense.

"Automatic driving" includes driving based on commands of a computer operation management system to which the automatic guided vehicle is connected by communication, and autonomous driving by a control device provided in the automatic guided vehicle. Autonomous travel includes not only travel by an automatic guided vehicle toward a destination along a predetermined route, but also travel following a tracking target. Also, the automatic guided vehicle may temporarily travel manually based on instructions from the operator. "Automated driving" generally includes both "guided" driving and "guideless" driving, but in this disclosure means "guideless" driving.

The “guide type” is a system in which derivatives are installed continuously or intermittently, and the guides are used to guide an automatic guided vehicle.

The “guideless type” is a method of guiding without installing a derivative. An automatic guided vehicle according to an embodiment of the present disclosure includes a self-position estimation device and can travel without a guide.

A "position estimation device" is a device that estimates its own position on an environmental map based on sensor data acquired by an external sensor such as a laser range finder.

The "external sensor" is a sensor that senses the state of the exterior of the moving object. External sensors include, for example, laser range finders (also called range sensors), cameras (or image sensors), LIDAR (Light Detection and Ranging), millimeter wave radars, ultrasonic sensors, and magnetic sensors.

An "internal sensor" is a sensor that senses the internal state of a moving object. Internal sensors include, for example, rotary encoders (hereinafter sometimes simply referred to as "encoders"), acceleration sensors, and angular acceleration sensors (eg, gyro sensors).

"SLAM" is an abbreviation for Simultaneous Localization and Mapping, which means performing self-localization and environmental map creation at the same time.

<Basic configuration of moving body in the present disclosure>
Please refer to FIG. The mobile object 10 of the present disclosure, in the exemplary embodiment shown in FIG. 1, includes an external sensor 102 that scans the environment and periodically outputs scan data. A typical example of the external sensor 102 is a laser range finder (LRF). The LRF periodically emits, for example, an infrared or visible laser beam into the environment to scan the surrounding environment. The laser beam is reflected by surfaces such as walls, structures such as columns, and objects placed on the floor. The LRF receives the reflected light of the laser beam, calculates the distance to each reflection point, and outputs measurement result data indicating the position of each reflection point. The position of each reflection point reflects the arrival direction and distance of the reflected light. The measurement result data (scan data) is sometimes called "environmental measurement data" or "sensor data".

The scanning of the environment by the external sensor 102 is performed, for example, on the environment within a range of 135 degrees to the left and right (270 degrees in total) with respect to the front of the external sensor 102 . Specifically, a pulsed laser beam is emitted while changing the direction at every predetermined step angle in a horizontal plane, and the reflected light of each laser beam is detected to measure the distance. If the step angle is 0.3 degrees, measurement data of the distance to the reflection point in the direction determined by the angle for a total of 901 steps can be obtained. In this example, the scanning of the surrounding space performed by the external sensor 102 is substantially parallel to the floor surface and planar (two-dimensional). However, the external sensor may perform three-dimensional scanning.

A typical example of scan data can be represented by the position coordinates of each point that constitutes a point cloud acquired for each scan. The position coordinates of the points are defined by a local coordinate system that moves together with the moving body 10 . Such a local coordinate system may be called a mobile coordinate system or a sensor coordinate system. In the present disclosure, the origin of the local coordinate system fixed to the mobile body 10 is defined as the “position” of the mobile body 10 and the orientation of the local coordinate system is defined as the “attitude” of the mobile body 10 . Hereinafter, the position and posture may be collectively referred to as "pose".

The scan data, when displayed in a polar coordinate system, may consist of a set of numerical values that indicate the location of each point in "direction" and "distance" from the origin in the local coordinate system. The polar coordinate system representation can be converted to a rectangular coordinate system representation. In the following explanation, for the sake of simplicity, it is assumed that the scan data output from the external sensor is displayed in an orthogonal coordinate system.

The moving object 10 includes a storage device 104 that stores an environment map and a position estimation device (self-position estimation) 106 . The environmental map may be divided into multiple maps. The position estimation device 106 performs matching between the scan data acquired from the external sensor 102 and the environment map read out from the storage device 104, and estimates the position and posture of the mobile object 10, that is, the pose. This matching is called pattern matching or scan matching and can be performed according to various algorithms. A typical example of a matching algorithm is the Iterative Closest Point (ICP) algorithm.

In the illustrated example, the mobile unit 10 further includes a drive device 108 , an automatic cruise control device 110 and a communication circuit 112 . The driving device 108 is a device that generates a driving force for moving the moving body 10 . Examples of drive devices 108 include wheels rotated by an electric motor or engine (drive wheels), bipedal or multipedal walking devices operated by motors or other actuators. The wheels may be omnidirectional wheels, such as Mecanum wheels. Further, the mobile body 10 may be a mobile body that moves in the air or water, or a hovercraft, and the drive device 108 in that case includes a propeller rotated by a motor.

The automatic cruise control device 110 operates the drive device 108 to control the movement conditions (speed, acceleration, movement direction, etc.) of the mobile body 10 . The automatic travel control device 110 may move the mobile body 10 along a predetermined travel route, or may move it according to a command given from the outside. The position estimation device 106 calculates estimated values of the position and orientation of the mobile body 10 while the mobile body 10 is moving or stopped. The automatic travel control device 110 controls travel of the moving body 10 by referring to this estimated value.

Position estimator 106 and automatic cruise controller 110 may be collectively referred to as cruise controller 120 . Travel control device 120 may be configured by a processor and a memory storing a computer program for controlling the operation of the processor. Such processors and memories may be implemented by one or more semiconductor integrated circuits.

The communication circuit 112 is a circuit for exchanging data and/or commands by connecting the mobile unit 10 to a communication network including an external management device, another mobile unit, or an operator's mobile terminal device.

<Environmental map>
FIG. 2 is a plan layout diagram schematically showing an example of an environment 200 in which the mobile object 10 moves. Environment 200 is part of a wider environment. In FIG. 2, thick straight lines indicate fixed walls 202 of a building, for example.

FIG. 3 is a diagram showing a map (environment map M) of the environment 200 shown in FIG. Each dot 204 in the figure corresponds to each point of the point group forming the environmental map M. In this disclosure, the environment map M point cloud may be referred to as the "reference point cloud" and the scan data point cloud may be referred to as the "data point cloud" or "source point cloud." Matching is, for example, aligning scan data (data point group) with an environment map (reference point group) whose position is fixed. When performing matching by the ICP algorithm, specifically, corresponding point pairs are selected between the reference point group and the data point group, and the distance (error) between the points forming each pair is minimized. , the position and orientation of the data point cloud are adjusted to .

In FIG. 3, the dots 204 are arranged on a plurality of line segments at regular intervals for simplicity. The point cloud in the real environment map M may have a more complicated arrangement pattern. The environmental map M is not limited to a point cloud map, and may be a map having straight lines or curved lines as constituent elements, or may be an occupation grid map. That is, the environment map M only needs to have a structure that enables matching between the scan data and the environment map M. FIG.

The scan data acquired by the external sensor 102 of the mobile body 10 when the mobile body 10 is at each of the positions PA, PB, and PC shown in FIG. 3 have different point group arrangements. When the movement time of the moving body 10 from the position PA to the position PC via the position PB is sufficiently longer than the scanning period of the external sensor 102, that is, when the movement of the moving body 10 is slow, the time axis The two adjacent scan data above are very similar. However, if the moving body 10 moves extremely fast, there is a possibility that two adjacent scan data on the time axis will be significantly different.

When the latest scan data among the scan data sequentially output from the external sensor 102 is similar to the immediately preceding scan data in this way, matching is relatively easy and can be performed in a short time with high reliability. A match is expected. However, if the moving speed of the moving body 10 is relatively high, the latest scan data may not be similar to the immediately preceding scan data, and the time required for matching may increase or the matching may be completed within a predetermined time. may not complete.

<Matching>
FIG. 4 is a diagram schematically showing an example of scan data SD(t) acquired by the external sensor at time t. The scan data SD(t) is displayed in a sensor coordinate system whose position and orientation change with the mobile object 10 . The scan data SD(t) is represented by a UV coordinate system whose V axis is directly in front of the external sensor 102 and whose U axis is rotated 90° clockwise from the V axis (see FIG. 9). The moving body 10, more precisely, the external sensor 102 is located at the origin of the UV coordinate system. In the present disclosure, when moving body 10 moves forward, moving body 10 moves directly in front of external sensor 102, that is, in the direction of the V-axis. For clarity, the points that make up the scan data SD(t) are indicated by white circles.

FIG. 5A is a diagram schematically showing a state when matching of scan data SD(t) with environment map M is started. The position and orientation of the moving object 10 shown in FIG. 5A are given as initial values at the start of matching. When this initial position is close to the actual position and orientation of the moving body 10, the time to reach the matching completion state is sufficiently short.

FIG. 5B is a diagram schematically showing a state in which matching of the scan data SD(t) with the environment map M is completed. When the matching is completed, the relationship between the position and orientation of the sensor coordinate system when the external sensor acquires the scan data SD(t) and the position and orientation of the environment map M coordinate system is determined. In this way, estimated values of the position (origin of the sensor coordinate system) and orientation (orientation of the sensor coordinate system) of the moving body 10 at time t are determined (position identification).

FIG. 6A is a diagram schematically showing how a point group that constitutes scan data rotates and translates from its initial position and approaches the point group of the environment map. Let Z _t,k be the coordinate value of the k-th point (k=1, 2, . Let m _k be the coordinate value of the point on the environmental map corresponding to . At this time, the error of the corresponding points in the two point groups can be evaluated using Σ(Z _t,k −m _k ) ² , which is the sum of the squares of the errors calculated for the K corresponding points, as a cost function. Rotational and translational rigid transformations are determined to reduce Σ(Z _t,k −m _k ) ² . A rigid transformation is defined by a transformation matrix (homogeneous transformation matrix) that includes the angle of rotation and the vector of translation as parameters.

FIG. 6B is a diagram showing the position and orientation of scan data after rigid transformation. In the example shown in FIG. 6B, matching between the scan data and the environment map is not complete, and there is still a large error (displacement) between the two point clouds. In order to reduce this misalignment, a rigid body transformation is further performed. Thus, matching is complete when the error is of a magnitude below a predetermined value.

FIG. 7 is a diagram schematically showing an example of scan data SD(t+Δt) acquired by the external sensor at time t+Δt. As in FIG. 4, the scan data SD(t+Δt) is displayed in a sensor coordinate system whose position and orientation change with the moving object 10, and the points forming the scan data SD(t+Δt) are indicated by white circles. When the position and orientation of the moving body 10 change significantly during time Δt, the arrangement of the point cloud of scan data acquired at time t+Δt is changed from the arrangement of the point cloud of scan data acquired at time t before that. can vary greatly. In this example, the placement of the point cloud shown in FIG. 7 has changed significantly from the placement of the point cloud shown in FIG. In such a case, if matching is started using the position and orientation of the mobile object 10 at time t as initial values, the difference between the scan data and the environment map may be too large, and the time required for matching may be lengthy.

FIG. 8A is a diagram schematically showing a state when matching of scan data SD(t+Δt) with environment map M is started.

On the other hand, FIG. 8B uses predicted values obtained by the calculation described below as the initial values of the position and orientation of the moving body 10 when matching of the scan data SD(t+Δt) with the environment map M is started. FIG. 10 is a diagram schematically showing a state in which

As is clear from FIG. 8B, the displacement of the scan data SD(t+Δt) with respect to the environment map M at the start of matching is smaller than in FIG. 8A. Therefore, the time required for matching is shortened.

<Initial value prediction>
Next, prediction of the position and orientation of the moving body 10 will be described with reference to FIG. FIG. 9 is a diagram schematically showing the history of the position and orientation of the moving object 10 obtained in the past by the position estimation device 106 of FIG. 1 and the predicted values of the current position and orientation. The position and attitude histories are stored in the internal memory of the position estimation device 10 . A part or all of such history may be stored in a storage device external to the position estimation device 10, such as the storage device 104 in FIG.

FIG. 9 also shows the UV coordinate system, which is the local coordinate system (sensor coordinate system) of the moving body 10 . Scan data is represented by a UV coordinate system. The position of the moving object 10 on the environment map M is the coordinate value (xi, yi) of the origin of the UV coordinate system in the environment map M coordinate system. The attitude (orientation) of the moving body 10 is the orientation (θi) of the UV coordinate system with respect to the environmental map M coordinate system. For θi, the counterclockwise direction is defined as "positive".

In the embodiment of the present disclosure, predicted values of the current position and orientation are calculated from the history of estimated positions and orientations obtained in the past by the position estimation device.

The estimated position and the estimated attitude of the moving object obtained by the previous matching are (x _i-1 , y _i-1 , θ _i-1 ), and the estimated position and the estimated attitude of the moving object obtained by the previous matching are be (x _i-2 , y _i-2 , θ _i-2 ). Let (x _i , y _i , θ _i ) be the predicted values of the current position and orientation of the moving object. At this time, suppose that the following assumption holds.

Assumption 1: The time required to move from the estimated position (x _i-1 , y _i-1 ) to the current position (x _i , y _i ) is the estimated position (x _i-2 , y _i-2 ) It is equal to the time required to move to (x _i-1 , y _i-1 ).

Assumption 2: The moving speed (rate of change of the estimated position) when moving from the estimated position (x _i-1 , y _i-1 ) to the current position (x _i , y _i ) is the estimated position (x _i-2 , y _i-2 ) to the estimated position (x _i-1 , y _i-1 ).

Assumption 3: A change in attitude (orientation) of the moving object θ _i -θ _i-1 is equal to Δθ=θ _i-1 -θ _i-2 .

ここで、Δθは、上述の通り、θ_i-1－θ_i-2である。Under the above process, the following Equation 1 is established.

Here, Δθ is θ _i-1 - θ _i-2 as described above.

Regarding the posture (orientation) of the moving object, the following relationship of Equation 2 holds from Assumption 3.
(Number 2)
θi ₌ θi _-1 + Δθ

By approximating that Δθ is zero, the calculation can be simplified assuming that the matrix of the second term on the right side of Equation 2 is a unit matrix.

If the above assumption 1 does not hold, the time required to move from the position (x _i-1 , y _i-1 ) to the position (x _i , y _i ) is Δt, and the position (x _i-2 , y _i-2 ) to the position (x _i-1 , y _i-1 ) is Δs. In this case, (x _i-1 −x _i-2 ) and (y _i-1 −y _i-2 ) on the right side of Equation 1 are corrected by multiplying Δt/Δs, respectively, and the matrix on the right side of Equation 1 is Correction may be performed by multiplying Δθ by Δt/Δs.

The position and attitude "history" is the time-series data of the estimates. The history may include estimated values with relatively low reliability or abnormal values that deviate greatly from the movement route. Such values may be excluded from the prediction calculation.

When the moving body moves according to an instruction from an operation management device as described later, it is also possible to use the contents of the instruction for prediction. Examples of instruction contents include specification of a straight or curved path, and specification of the speed of movement.

In addition, by using not only the history of the short period from time t−Δs to time t, but also the history of a longer period, prediction with higher accuracy and precision becomes possible. For example, the estimated position and orientation of the moving object at three or more different times may be selected from the history to predict the position and orientation of the moving object.

The method of predicting the current position and orientation from the history of the estimated position and orientation of the moving object is not limited to computation. This prediction may be performed using a trained model prepared by machine learning such as deep learning.

<Operation flow of position estimation device>
The operation flow of the position estimation device according to the embodiment of the present disclosure will be described with reference to FIGS. 1, 10 and 11. FIG.

First, refer to FIG.

In step S<b>22 , the position estimation device 106 acquires scan data from the external sensor 102 .

In step S24, position estimator 106 predicts the current position and orientation from the history in the manner described above and determines the predicted values.

In step S26, the position estimator 106 performs initial alignment using the predicted values.

In step S28, the position estimation device 106 corrects the positional deviation by the ICP algorithm.

Next, referring to FIG. 11, positional deviation correction in step S28 will be described.

First, in step S32, a search for corresponding points is performed. Specifically, a point on the environment map corresponding to each point forming the point group included in the scan data is selected.

In step S34, the scan data is subjected to rigid transformation (coordinate transformation) of rotation and translation so as to reduce the distance between corresponding points between the scan data and the environment map. This is to optimize the parameters of the coordinate transformation matrix so as to reduce the distance between corresponding points, that is, the sum of errors (sum of squares) of corresponding points. This optimization is done by iterative calculations.

In step S36, it is determined whether or not the result of the iterative calculation has converged. Specifically, it is determined that convergence has occurred when the amount of decrease in the sum of errors (sum of squares) of corresponding points is less than a predetermined value even when the parameters of the coordinate transformation matrix are changed. If it does not converge, the process returns to step S32, and the process from the search for the corresponding points is repeated. When it is determined in step S36 that convergence has occurred, the process proceeds to step S38.

In step S38, the coordinate conversion matrix is used to convert the coordinate values of the scan data from the sensor coordinate system values to the environmental map coordinate system values. The coordinate values of the scan data thus obtained can be used to update the environmental map.

According to the moving body of the present disclosure, even when the moving speed of the moving body 10 is high and the change in the scan data periodically acquired from the external sensor 102 is significant, the time required for matching is greatly increased, or the matching is not performed. Possibility of failure in localization due to failure to complete within a predetermined time is reduced.

In addition, there is no need to estimate the position and orientation using the output of an internal sensor such as a rotary encoder. A rotary encoder, in particular, generates a large error when a wheel slips, and the error is accumulated, so the reliability of the measured value is low. Furthermore, measurements by rotary encoders are not applicable to mobile objects that move using omnidirectional wheels such as mecanum wheels, bipedal or multipedal locomotion devices, or flying objects such as barcraft and drones. On the other hand, the position estimation device according to the present disclosure can be applied to various moving objects that are moved by various driving devices.

The moving body of the present disclosure does not need to be equipped with a driving device. For example, it may be a wheelbarrow driven by the user. Such a moving object may display the position or position and orientation of the moving object obtained from the position estimating device on a map of the display device for the user, or may communicate by voice.

FIG. 12 is a diagram showing another configuration example of the mobile body 10 of the present disclosure. The moving body 10 shown in this figure is provided with a navigation device 114 that guides the user using the position or position and orientation information of the moving body output from the position estimation device 106 . The navigation device 114 is connected to a display device 116 for guiding the user by means of images or sounds. The display device 116 can display the current position and the target position on a map, and emit sounds such as "stop" or "turn right". Therefore, the user can push the mobile object 10 to move it to the destination. Examples of such vehicles 10 are wheelbarrows and other carts.

According to the mobile body 10 having the configuration shown in FIG. 12, route guidance is executed based on the destination or route pre-stored in the memory (not shown) within the navigation device 114 .

<Exemplary embodiment>
In the following, embodiments of mobile objects according to the present disclosure will be described in more detail. In this embodiment, an automatic guided vehicle is taken as an example of a mobile object. In the following description, an abbreviation is used to describe an automatic guided vehicle as an "AGV (Automatic Guided Vehicle)". Hereinafter, "AGV" will also be denoted by reference numeral "10" in the same manner as the moving body 10. As shown in FIG.

(1) Basic Configuration of System FIG. 13 shows a basic configuration example of an exemplary mobile management system 100 according to the present disclosure. The mobile management system 100 includes at least one AGV 10 and an operation management device 50 that manages operation of the AGV 10 . A terminal device 20 operated by the user 1 is also shown in FIG.

The AGV 10 is an unmanned guided vehicle capable of "guideless" travel that does not require a dielectric such as a magnetic tape for travel. The AGV 10 can estimate its own position and transmit the estimation result to the terminal device 20 and the operation management device 50 . The AGV 10 can automatically travel within the environment S in accordance with commands from the operation management device 50 .

The operation management device 50 is a computer system that tracks the position of each AGV 10 and manages the running of each AGV 10 . The traffic management device 50 can be a desktop PC, a notebook PC, and/or a server computer. The operation management device 50 communicates with each AGV 10 via multiple access points 2 . For example, the operation management device 50 transmits to each AGV 10 the coordinate data of the position to which each AGV 10 should go next. Each AGV 10 periodically transmits data indicating its own position and orientation to the operation management device 50, for example, every 250 milliseconds. When the AGV 10 reaches the instructed position, the operation management device 50 further transmits coordinate data of the position to which the vehicle should go next. The AGV 10 can also travel within the environment S according to the user's 1 operation input to the terminal device 20 . An example of the terminal device 20 is a tablet computer.

FIG. 14 shows an example of an environment S in which three AGVs 10a, 10b and 10c are present. It is assumed that both AGVs are running in the depth direction in the figure. The AGVs 10a and 10b are in the process of transporting loads placed on the top plate. The AGV 10c is running following the AGV 10b in front. For convenience of explanation, reference numerals 10a, 10b and 10c are attached in FIG. 14, but they are described as "AGV 10" below.

The AGV 10 can also transport a load by using a towing truck connected to itself, in addition to the method of transporting the load placed on the top board. FIG. 15 shows the AGV 10 and the tow truck 5 before being connected. Each leg of the tow truck 5 is provided with a caster. The AGV 10 is mechanically connected with the towing truck 5 . FIG. 16 shows the AGV 10 and towing truck 5 connected. When the AGV 10 travels, the tow truck 5 is towed by the AGV 10. - 特許庁By towing the tow truck 5, the AGV 10 can transport the load placed on the tow truck 5. - 特許庁

The connection method between the AGV 10 and the towing truck 5 is arbitrary. An example is described here. A plate 6 is fixed to the top plate of the AGV 10 . The towing carriage 5 is provided with a guide 7 having a slit. The AGV 10 approaches the tow truck 5 and inserts the plate 6 into the slit in the guide 7. When the insertion is completed, the AGV 10 causes an electromagnetic locking pin (not shown) to pass through the plate 6 and the guide 7 to apply an electromagnetic lock. As a result, the AGV 10 and the towing truck 5 are physically connected.

Refer to FIG. 13 again. Each AGV 10 and the terminal device 20 are connected, for example, one-to-one, and can perform communication conforming to the Bluetooth (registered trademark) standard. Each AGV 10 and the terminal device 20 can also perform Wi-Fi (registered trademark) compliant communication using one or a plurality of access points 2 . A plurality of access points 2 are connected to each other via a switching hub 3, for example. FIG. 13 shows two access points 2a and 2b. The AGV 10 is wirelessly connected to the access point 2a. The terminal device 20 is wirelessly connected to the access point 2b. The data transmitted by the AGV 10 is received by the access point 2a, transferred to the access point 2b via the switching hub 3, and transmitted to the terminal device 20 from the access point 2b. Also, the data transmitted by the terminal device 20 is received by the access point 2b, transferred to the access point 2a via the switching hub 3, and transmitted from the access point 2a to the AGV 10. FIG. Thereby, bidirectional communication between the AGV 10 and the terminal device 20 is realized. A plurality of access points 2 are also connected to an operation management device 50 via a switching hub 3 . Thereby, two-way communication is also realized between the operation management device 50 and each AGV 10 .

(2) Creation of Environment Map A map of the environment S is created so that the AGV 10 can travel while estimating its own position. The AGV 10 is equipped with a position estimator and an LRF, and the output of the LRF can be used to create a map.

The AGV 10 transitions to the data acquisition mode by user's operation. In data acquisition mode, the AGV 10 begins acquiring sensor data using the LRF.

The position estimation device accumulates sensor data in a storage device. When acquisition of the sensor data in the environment S is completed, the sensor data accumulated in the storage device are transmitted to the external device. The external device is for example a computer having a signal processor and installed with a cartographic computer program.

A signal processor in the external device superimposes the sensor data obtained for each scan. A map of the environment S can be created by repeating the process of superimposition by the signal processor. The external device transmits the created map data to the AGV 10 . The AGV 10 saves the created map data in an internal storage device. The external device may be the operation management device 50 or may be another device.

The AGV 10 may create the map instead of the external device. A circuit such as a microcontroller unit (microcomputer) of the AGV 10 may perform the processing performed by the signal processing processor of the external device described above. When creating a map within the AGV 10, there is no need to transmit the accumulated sensor data to an external device. The data volume of sensor data is generally considered to be large. Since there is no need to transmit sensor data to an external device, occupation of communication lines can be avoided.

Note that movement within the environment S for acquiring sensor data can be realized by the AGV 10 running according to the user's operation. For example, the AGV 10 wirelessly receives a travel command from the user via the terminal device 20 to move in the front, back, left, and right directions. The AGV 10 travels forward, backward, left and right in the environment S according to the travel command to create a map. When the AGV 10 is wired to a control device such as a joystick, the AGV 10 may travel forward, backward, left and right in the environment S according to control signals from the control device to create a map. Sensor data may be acquired by a person pushing a measurement trolley equipped with an LRF.

Although a plurality of AGVs 10 are shown in FIGS. 13 and 14, the number of AGVs may be one. When a plurality of AGVs 10 exist, the user 1 can use the terminal device 20 to select one AGV 10 from among the plurality of registered AGVs and create a map of the environment S.

After the map is created, each AGV 10 can automatically travel while estimating its own position using the map.

(3) Configuration of AGV FIG. 17 is an external view of an exemplary AGV 10 according to this embodiment. The AGV 10 has two drive wheels 11a and 11b, four casters 11c, 11d, 11e and 11f, a frame 12, a carrier table 13, a travel control device 14, and an LRF 15. Two drive wheels 11a and 11b are provided on the right and left sides of the AGV 10, respectively. Four casters 11c, 11d, 11e and 11f are arranged at the four corners of the AGV 10. As shown in FIG. Note that the AGV 10 also has multiple motors connected to the two drive wheels 11a and 11b, but the multiple motors are not shown in FIG. FIG. 17 also shows one drive wheel 11a and two casters 11c and 11e positioned on the right side of the AGV 10 and a caster 11f positioned on the left rear, but the left drive wheel 11b and the left front The caster 11d is hidden behind the frame 12 and is not shown. The four casters 11c, 11d, 11e and 11f are freely swivelable. In the following description, the drive wheels 11a and 11b are also referred to as wheels 11a and 11b, respectively.

The travel control device 14 is a device that controls the operation of the AGV 10, and mainly includes an integrated circuit including a microcomputer (described later), electronic components, and a board on which they are mounted. The traveling control device 14 performs data transmission/reception with the terminal device 20 and preprocessing calculations as described above.

The LRF 15 is an optical device that emits an infrared laser beam 15a, for example, and detects the reflected light of the laser beam 15a to measure the distance to the reflection point. In this embodiment, the LRF 15 of the AGV 10 radiates a pulsed laser beam 15a into a space within a range of 135 degrees (270 degrees in total) from the front of the AGV 10, for example, while changing the direction every 0.25 degrees. Then, the reflected light of each laser beam 15a is detected. As a result, it is possible to obtain data on the distance to the reflection point in the direction determined by the angles of 1081 steps in total, every 0.25 degrees. In this embodiment, the scanning of the surrounding space performed by the LRF 15 is substantially parallel to the floor surface and planar (two-dimensional). However, the LRF 15 may perform vertical scanning.

The AGV 10 can create a map of the environment S from the position and orientation (orientation) of the AGV 10 and the scan results of the LRF 15 . The map may reflect the placement of the walls around the AGV, structures such as pillars, and objects placed on the floor. Map data is stored in a storage device provided in the AGV 10 .

The position and orientation of the AGV 10, ie pose (x, y, θ), may hereinafter be simply referred to as "position".

As described above, the travel control device 14 compares the measurement result of the LRF 15 with the map data held by itself, and estimates its own current position. The map data may be map data created by another AGV 10 .

FIG. 18A shows a first hardware configuration example of the AGV 10. As shown in FIG. FIG. 18A also shows a specific configuration of the travel control device 14. As shown in FIG.

The AGV 10 includes a travel control device 14, an LRF 15, two motors 16a and 16b, a driving device 17, and wheels 11a and 11b.

The traveling control device 14 has a microcomputer 14a, a memory 14b, a storage device 14c, a communication circuit 14d, and a position estimation device 14e. The microcomputer 14a, the memory 14b, the storage device 14c, the communication circuit 14d, and the position estimation device 14e are connected by a communication bus 14f, and can exchange data with each other. The LRF 15 is also connected to the communication bus 14f via a communication interface (not shown), and transmits measurement data, which are measurement results, to the microcomputer 14a, the position estimation device 14e and/or the memory 14b.

The microcomputer 14a is a processor or control circuit (computer) that performs calculations for controlling the entire AGV 10 including the travel control device 14. FIG. The microcomputer 14a is typically a semiconductor integrated circuit. The microcomputer 14a transmits a PWM (Pulse Width Modulation) signal, which is a control signal, to the driving device 17 to control the driving device 17 and adjust the voltage applied to the motor. This causes each of the motors 16a and 16b to rotate at the desired rotational speed.

One or more control circuits (for example, microcomputers) for controlling the driving of the left and right motors 16a and 16b may be provided independently of the microcomputer 14a. For example, the motor driving device 17 may include two microcomputers that respectively control the driving of the motors 16a and 16b.

The memory 14b is a volatile storage device that stores computer programs executed by the microcomputer 14a. The memory 14b can also be used as a work memory when the microcomputer 14a and the position estimation device 14e perform calculations.

The storage device 14c is a non-volatile semiconductor memory device. However, the storage device 14c may be a magnetic recording medium typified by a hard disk, or an optical recording medium typified by an optical disc. Further, the storage device 14c may include a head device for writing data to and/or reading data from any recording medium and a control device for the head device.

The storage device 14c stores an environment map M of an environment S in which the vehicle travels, and data (travel route data) R of one or more travel routes. The environment map M is created by the AGV 10 operating in the map creation mode and stored in the storage device 14c. The travel route data R is transmitted from the outside after the environmental map M is created. Although the environment map M and the travel route data R are stored in the same storage device 14c in this embodiment, they may be stored in different storage devices.

An example of travel route data R will be described.

If the terminal device 20 is a tablet computer, the AGV 10 receives travel route data R indicating a travel route from the tablet computer. The travel route data R at this time includes marker data indicating the positions of a plurality of markers. A "marker" indicates a passing position (way point) of the traveling AGV 10 . The travel route data R includes at least position information of a start marker indicating a travel start position and an end marker indicating a travel end position. The travel route data R may further include position information of markers of one or more intermediate waypoints. When the travel route includes one or more intermediate waypoints, the route from the start marker to the end marker via the travel waypoints in order is defined as the travel route. The data of each marker can include the direction (angle) and travel speed data of the AGV 10 until it moves to the next marker, in addition to the coordinate data of that marker. When the AGV 10 temporarily stops at the position of each marker and performs self-position estimation and notification to the terminal device 20, etc., the data of each marker is the acceleration time required for acceleration to reach the running speed, and / or , data of the deceleration time required for deceleration from the running speed to stop at the position of the next marker.

The movement of the AGV 10 may be controlled by the operation management device 50 (eg, PC and/or server computer) instead of the terminal device 20 . In that case, the operation management device 50 may instruct the AGV 10 to move to the next marker each time the AGV 10 reaches the marker. For example, the AGV 10 receives, from the operation management device 50, the coordinate data of the next target position, or the data of the distance to the target position and the angle to be traveled, as the travel route data R indicating the travel route.

The AGV 10 can travel along the stored travel route while estimating its own position using the created map and the sensor data output by the LRF 15 acquired during travel.

The communication circuit 14d is, for example, a wireless communication circuit that performs wireless communication conforming to the Bluetooth (registered trademark) and/or Wi-Fi (registered trademark) standards. Both standards include wireless communication standards using frequencies in the 2.4 GHz band. For example, in a mode in which the AGV 10 is driven to create a map, the communication circuit 14d performs wireless communication conforming to the Bluetooth (registered trademark) standard and communicates with the terminal device 20 on a one-to-one basis.

The position estimation device 14e performs map creation processing and self-position estimation processing during travel. The position estimation device 14e creates a map of the environment S based on the position and orientation of the AGV 10 and the LRF scan results. During running, the position estimating device 14e receives sensor data from the LRF 15 and reads the environment map M stored in the storage device 14c. By matching local map data (sensor data) created from the scan results of the LRF 15 with a wider environmental map M, the self position (x, y, θ) on the environmental map M is identified. The position estimator 14e generates "confidence" data representing the extent to which the local map data matches the environmental map M. FIG. Self-position (x, y, θ) and reliability data can be transmitted from the AGV 10 to the terminal device 20 or the operation management device 50 . The terminal device 20 or the operation management device 50 can receive the self-position (x, y, θ) and reliability data and display them on a built-in or connected display device.

In this embodiment, the microcomputer 14a and the position estimation device 14e are separate components, but this is just an example. A single chip circuit or a semiconductor integrated circuit capable of independently performing each operation of the microcomputer 14a and the position estimation device 14e may be used. FIG. 18A shows a chip circuit 14g that encompasses the microcomputer 14a and the position estimator 14e. An example in which the microcomputer 14a and the position estimation device 14e are provided independently will be described below.

Two motors 16a and 16b are attached to the two wheels 11a and 11b respectively to rotate each wheel. That is, the two wheels 11a and 11b are drive wheels respectively. Motors 16a and 16b are described herein as being motors that drive the right and left wheels of AGV 10, respectively.

The drive device 17 has motor drive circuits 17a and 17b for adjusting the voltage applied to each of the two motors 16a and 16b. Each of motor drive circuits 17a and 17b includes a so-called inverter circuit. The motor drive circuits 17a and 17b turn on or off the current flowing through each motor according to the PWM signal sent from the microcomputer 14a or the microcomputer in the motor drive circuit 17a, thereby adjusting the voltage applied to the motor.

FIG. 18B shows a second hardware configuration example of the AGV 10. As shown in FIG. The second hardware configuration example differs from the first hardware configuration example (FIG. 18A) in that it has a laser positioning system 14h and that the microcomputer 14a is connected to each component on a one-to-one basis. do.

Laser positioning system 14 h has position estimator 14 e and LRF 15 . The position estimation device 14e and the LRF 15 are connected, for example, by an Ethernet (registered trademark) cable. Each operation of the position estimation device 14e and the LRF 15 is as described above. The laser positioning system 14h outputs information indicating the pose (x, y, θ) of the AGV 10 to the microcomputer 14a.

The microcomputer 14a has various general-purpose I/O interfaces or general-purpose input/output ports (not shown). The microcomputer 14a is directly connected to other components in the travel control device 14, such as the communication circuit 14d and the laser positioning system 14h, via the general-purpose input/output port.

The configuration is common to that of FIG. 18A except for the configuration described above with respect to FIG. 18B. Therefore, the description of the common configuration is omitted.

The AGV 10 in embodiments of the present disclosure may include safety sensors, such as obstacle detection sensors and bumper switches, not shown.

(4) Configuration Example of Traffic Management Device FIG. 19 shows a hardware configuration example of the traffic management device 50 . The operation management device 50 has a CPU 51 , a memory 52 , a position database (position DB) 53 , a communication circuit 54 , a map database (map DB) 55 and an image processing circuit 56 .

The CPU 51, memory 52, position DB 53, communication circuit 54, map DB 55, and image processing circuit 56 are connected by a communication bus 57, and can exchange data with each other.

The CPU 51 is a signal processing circuit (computer) that controls the operation of the operation management device 50 . The CPU 51 is typically a semiconductor integrated circuit.

The memory 52 is a volatile storage device that stores computer programs executed by the CPU 51 . The memory 52 can also be used as a work memory when the CPU 51 performs calculations.

The position DB 53 stores position data indicating each potential destination of each AGV 10 . The position data may be represented by coordinates set virtually within the factory by an administrator, for example. Location data is determined by an administrator.

Communication circuit 54 performs wired communication conforming to the Ethernet (registered trademark) standard, for example. The communication circuit 54 is connected to the access point 2 ( FIG. 13 ) by wire, and can communicate with the AGV 10 via the access point 2 . The communication circuit 54 receives data to be transmitted to the AGV 10 from the CPU 51 via the bus 57 . The communication circuit 54 also transmits data (notification) received from the AGV 10 to the CPU 51 and/or the memory 52 via the bus 57 .

The map DB 55 stores map data of the inside of a factory or the like where the AGV 10 travels. The data format does not matter as long as the map has a one-to-one correspondence with the position of each AGV 10 . For example, the map stored in the map DB 55 may be a map created by CAD.

The position DB 53 and the map DB 55 may be constructed on a non-volatile semiconductor memory, or may be constructed on a magnetic recording medium typified by a hard disk, or an optical recording medium typified by an optical disc.

The image processing circuit 56 is a circuit that generates image data to be displayed on the monitor 58 . The image processing circuit 56 operates exclusively when the manager operates the operation management device 50 . In this embodiment, a more detailed description is omitted. Note that the monitor 58 may be integrated with the operation management device 50 . Alternatively, the processing of the image processing circuit 56 may be performed by the CPU 51 .

In the description of the above embodiments, an AGV that travels in a two-dimensional space (floor surface) is taken as an example. However, the present disclosure can also be applied to mobile objects that move in three-dimensional space, such as flying objects (drones). A two-dimensional space can be extended to a three-dimensional space when a drone creates a three-dimensional space map while flying.

The above generic aspects may be implemented by systems, methods, integrated circuits, computer programs, or recording media. Alternatively, it may be implemented by any combination of systems, devices, methods, integrated circuits, computer programs, and recording media.

The mobile body of the present disclosure can be suitably used for moving and transporting goods such as packages, parts, and finished products in factories, warehouses, construction sites, logistics, hospitals, and the like.

Reference Signs List 1 User, 2a, 2b Access point, 10 AGV (moving body), 11a, 11b Driving wheel (wheel), 11c, 11d, 11e, 11f Caster, 12 Frame 13 Carrying table 14 Travel control device 14a Microcomputer 14b Memory 14c Storage device 14d Communication circuit 14e Position estimation device 16a, 16b motor 15 laser range finder 17a, 17b motor drive circuit 20 terminal device (mobile computer such as tablet computer) 50 operation Management device 51 CPU 52 memory 53 position database (position DB) 54 communication circuit 55 map database (map DB)

Claims (11)

being mobile,
an external sensor that scans the environment and periodically outputs scan data;
a storage device that stores an environment map;
a position estimation device that performs matching between the scan data and the environment map read from the storage device to estimate the position and orientation of the mobile body;
with
The position estimation device,
determining a predicted value of the current position and orientation of the moving object based on the history of the estimated position and the estimated orientation of the moving object obtained by past matching;
A moving body that uses the predicted values as initial values to start matching for estimating the current position and attitude of the moving body. The position estimation device,
2. The method according to claim 1, wherein a rate of change of said estimated position and said estimated attitude is determined based on a history of said estimated position and said estimated attitude of said moving body, and said position and attitude of said moving body are predicted based on said rate of change. mobile. 3. The mobile body according to claim 1, wherein said position estimation device performs said matching by an iterative closest point algorithm. 4. The moving object according to any one of claims 1 to 3, comprising either an omnidirectional wheel, a bipedal or multipedal walking device. 5. The moving body according to any one of claims 1 to 4, further comprising a display device for displaying said position, or said position and said orientation of said moving body estimated by said position estimation device, by means of images and/or sounds. 6. The moving object according to claim 5, comprising a navigation device that guides a user on a route based on a destination and said position and said attitude of said moving object estimated by said position estimation device. The position estimation device,
when determining the rate of change of the estimated position and the estimated posture based on the history of the estimated position and the estimated posture of the moving body, selecting the estimated position or the estimated posture included in the history according to reliability; The moving object according to claim 2. 7. The position estimating device according to any one of claims 1 to 6, wherein, when moving in accordance with an instruction from an operation management device, said position estimating device predicts the position and orientation of said moving object based on the content of said instruction and said history. Mobile. 7. The position estimating device according to any one of claims 1 to 6, wherein the position estimation device selects the estimated position and the estimated orientation of the moving object at three or more different times from the history to predict the position and the orientation of the moving object. The moving body described in . A mobile body position estimation device connected to an external sensor that scans the environment and periodically outputs scan data and a storage device that stores an environment map,
a processor;
a memory storing a computer program for operating the processor;
with
The processor, according to instructions of the computer program,
Predicting the current position and orientation of the moving object based on a history of the estimated position and the estimated orientation of the moving object obtained by past matching between the scan data and the environment map read from the storage device. determining a value; and using the predicted value as an initial value to initiate matching for estimating the current position and attitude of the moving object . A computer program used in the position estimation device according to claim 10.

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